Currently position of mobile devices can be computed using satellite based Global Positioning System (GPS). A GPS receiver in the mobile device may be configured for obtaining positioning information related to the mobile device. Although successful, the GPS receiver may not provide accurate positioning information in case of signal blockages caused in tunnels, deep-urban areas, and foliages.
In case of indoor positioning, the GPS may not work as signals transmitted by the satellites associated with the GPS may get attenuated and scattered by roofs, walls, and other objects in an indoor environment. Therefore, a method of indoor positioning based on Wireless Fidelity (Wi-Fi) signals transmitted by wireless access points was developed.
A Wi-Fi based positioning system may use radio signals from an Access Point (AP) at the mobile device for computing position of the mobile device in the indoor environment. Examples of indoor environment can include office buildings, residential apartments, cinema halls, malls or the like. The mobile device can measure a Received Signal Strength Indicator (RSSI) from the plurality of AP's and perform a trilateration based calculation to compute its position. There are two approaches primarily used for the position computation. The first is a finger-printing approach, where the entire area of the indoor environment needs to be calibrated. It requires comparison of the RSSI data received from current measurements with pre-measured data collected at calibrated locations. Any change in the location of the AP in the indoor environment reduces the accuracy of the position calculation, and requires recalibration of the indoor environment.
The second is the propagation model based approach, where the distances to the APs are calculated based on the received signal strengths at the mobile device, which are subsequently used by trilateration algorithms to compute the position of the mobile device, which can be used to determine the position the user of the mobile device. The performance of both these approaches is strongly influenced by the environment. A change in the environment has an undue effect in determining the position of the mobile device. These approaches consume a lot of battery power due to the processing of position calculation algorithms and also undergo significant delays, in the order of 1 to 10 secs, to compute the position of the user. Further, for the location information of the APs, a database containing the MAC ID's and location data of the AP's is required at the mobile device. Moreover, scanning for the AP's incurs processing power and latencies.
The above information is presented as background information only to help the reader to understand the present invention. Applicants have made no determination and make no assertion as to whether any of the above might be applicable as Prior Art with regard to the present application.